Earticle

초록

영어

In this paper, we present a new approach to reconstruct a high resolution (HR) image from a low resolution (LR) input image based on a two dimensional (2D) sparse method. The new method consists of three phases. Firstly, the nonlinear feature of the input LR image is divided into the linear subspace, and then LR-HR dictionaries are learned to reduce the blurred artifacts of the image. Secondly, 2D sparse representation and self-similarity are developed to strengthen and enhance the image structure. Finally, the final HR image is achieved by reconstruction of all HR patches. Simulation results demonstrated that our proposed method achieved superior results on real images, and shows various improvements in terms of PSNR and SSIM values as compared with some other competent methods.

목차

Abstract
 1. Introduction
 2. Problem Formulation and Modeling
  2.1. Subspace Modeling
  2.2. LR-HR Dictionary
 3. 2D Sparse Model
  3.1. Self-similarity Features
  3.2. 2D Dictionary
 4. Image Reconstruction
 5. Simulation Results
  5.1. Simulation Results with Different Images
  5.2. Visual Analysis with Different Patch Size
  5.3. Simulation Results Comparison in Terms of PSNR and RMSE
 6. Conclusions
 References

키워드

저자

• Muhammad Sameer Sheikh [ Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China ]
• Qunsheng Cao [ Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605